Emission Analysis of Electric Motorcycles and Assessment of Emission Reduction With Fleet Electrification

Ho, Yu‐Hsuan; Hsiao, Ta-Chih; Chen, Albert Y.

doi:10.1109/tits.2023.3272385

Cited by 2 publications

(1 citation statement)

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“…The performance of the classifier is evaluated by precision, recall, and F1-score in macro-, micro, and the value of AUC. Definitions of these evaluations can be referenced in the work of Ho et al 65 . The random forest classifier is capable of modeling, almost entirely, the clustering results, with AUC.…”

Section: Parameters Settingmentioning

confidence: 99%

Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features

Mostafavi,

kai

2023

Preprint

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Community resilience is a complex and muti-faceted phenomenon that emerges from complex and nonlinear interactions among different socio-technical systems and their resilience properties. However, present studies on community resilience focus primarily on vulnerability assessment and utilize index-based approaches, with limited ability to capture heterogeneous features within community socio-technical systems and their nonlinear interactions in shaping robustness, redundancy, and resourcefulness components of resilience. To address this gap, this paper presents an integrated three-layer deep learning model for community resilience rating (called Resili-Net). Twelve measurable resilience features are specified and computed within community socio-technical systems (i.e., facilities, infrastructures, and society) related to three resilience components of robustness, redundancy, and resourcefulness. Using publicly accessible data from multiple metropolitan statistical areas in the United States, Resili-Netcharacterizes the resilience levels of spatial areas into five distinct levels. The interpretability of the model outcomes enables feature analysis for specifying the determinants of resilience in areas within each resilience level, allowing for the identification of specific resilience enhancement strategies. Changes in community resilience profiles under urban development patterns are further examined by changing the value of related socio-technical systems features. Also, combined resilience-risk levels in each community are analyzed, and several communities are found to suffer from high risk and low resilience, which calls for special attention to resilience enhancement. Departing from the dominantly vulnerability-focused assessments, Resili-Net enables characterizing community resilience as an emergent property arising from nonlinear interactions among heterogeneous community features related to their socio-technical systems. Accordingly, the outcomes provide novel perspectives for community resilience assessment by harnessing machine intelligence and heterogeneous urban big data.

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Section: Parameters Settingmentioning

confidence: 99%